There is widespread recognition that significant savings in electrical energy consumption can be achieved by better energy management and control in residential and commercial buildings. To achieve more efficient energy control, it would be helpful to have reasonably accurate real-time monitoring of the electrical loads (e.g., appliances, lighting devices, motors, etc.) in use. (Hereafter, the species term “appliance” is used interchangeably with the generic term “load,” unless the context indicates a specific type of appliance is intended.) One way to obtain load-specific information about energy utilization is to monitor each load individually. However, this requires that energy usage be monitored at each appliance, either with apparatus internal to the appliance (thus raising the cost per appliance, especially to retrofit already installed appliances) or requiring an add-on device per appliance. Another way is to monitor total electricity consumption in the aggregate, at the main breaker or service feed level, and then disaggregating the data to separate out the timing and contribution of each load of interest. Armed with that information, attention can be given to more efficient usage of electrical energy, such as the savings that can be achieved by taking various steps (from changing an appliance to another model or time-shifting the use of an appliance, to any of a number of other measures). However, disaggregating the overall energy consumption into its constituent parts is a non-trivial task.
Such nonintrusive appliance load monitoring (NIALM) methods require both hardware (including a sensor and other elements) and software (i.e., signal processing algorithms executing on one or more processors) components. The software component of NIALM depends on the hardware component. For example, signal waveform analysis can be used if the sensor samples voltage and current at a rate of at least several kHz. However, such sensors are still expensive and not widely accessible. An inexpensive and easy-to-install hardware alternative is a sensor that measures the total electric power being delivered to a residential or commercial premise at a sampling frequency of about 1 Hz. NIALM algorithms corresponding to such sensor have been detailed in, for example, U.S. Pat. Nos. 4,858,141; 5,717,325; 5,483,153 and in several academic publications. These algorithms detect step changes in power and match these changes with the loads being turned on or off. The changes in power can either be one-dimensional (e.g., only real power is measured) or two-dimensional (e.g., both real and reactive power components are measured).
Even though these NIALM algorithms are capable of monitoring major household appliances and the like, their accuracy level is only in the neighborhood of 80%. The main reason for the monitoring accuracy being that low is that two or more different loads may be operated concurrently and even may be switched on or off in very close time proximity. Further, the amount of power two different loads consume could be quite similar. For example, the power draw of a computer monitor could approximately equal that of an incandescent bulb, which makes these two loads indistinguishable by the mentioned algorithms. Another significant cause for the low accuracy is that the mentioned algorithms consider one-for-one matching between a power change and the switching of a load state. This matching is prone to both measurement and algorithmic errors and to the ambiguity which results from a simultaneous starting or stopping of multiple loads.
Accordingly, a need exists for a NIALM system and method that can provide more accurate appliance (load) usage data.